\begin{table}

\caption{\label{tab:unnamed-chunk-1}Predicting the number of texts about housing using negative binomial regression models. All models include year fixed effects. Robust standard errors, clustered by politician, in parentheses. \label{tab:modcount}}
\centering
\begin{tabular}[t]{lccc}
\toprule
  & M1: Combined & M2: Tweets & M3: PQs\\
\midrule
Landlord (ref.: No Landlord) & \num{0.10} & \num{0.05} & \num{0.07}\\
 & (\num{0.10}) & (\num{0.15}) & \vphantom{1} (\num{0.13})\\
Opposition (ref.: Government) & \num{0.42}*** & \num{0.00} & \num{0.68}***\\
 & (\num{0.10}) & (\num{0.15}) & (\num{0.13})\\
Dublin Constituencies & \num{0.25}* & \num{0.55}** & \num{-0.03}\\
 & (\num{0.12}) & (\num{0.19}) & (\num{0.13})\\
Seniority (Years) & \num{0.00} & \num{-0.02}* & \num{0.02}\\
 & (\num{0.01}) & (\num{0.01}) & (\num{0.01})\\
Change in Property Prices & \num{0.00} & \num{0.00} & \num{0.00}\\
 & (\num{0.00}) & (\num{0.00}) & (\num{0.00})\\
\midrule
Num.Obs. & \num{1580} & \num{1339} & \num{1377}\\
R2 & \num{0.007} & \num{0.008} & \num{0.012}\\
R2 Adj. & \num{0.006} & \num{0.006} & \num{0.010}\\
AIC & \num{22928.4} & \num{17959.4} & \num{18510.9}\\
BIC & \num{23008.8} & \num{18037.4} & \num{18589.3}\\
RMSE & \num{546.34} & \num{448.95} & \num{342.09}\\
\bottomrule
\multicolumn{4}{l}{\rule{0pt}{1em}* p $<$ 0.05, ** p $<$ 0.01, *** p $<$ 0.001}\\
\end{tabular}
\end{table}
